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Tensor Field Visualization,
Topological Data Analysis
GPU-Based Interactive Cut-Surface Extraction From High-0rder Finite Element Fields|
B. Nelson, R. Haimes, R.M. Kirby. In IEEE Transactions on Visualization and Computer Graphics (IEEE Visualization Issue), Vol. 17, No. 12, pp. 1803--1811. 2011.
We present a GPU-based ray-tracing system for the accurate and interactive visualization of cut-surfaces through 3D simulations of physical processes created from spectral/hp high-order finite element methods. When used by the numerical analyst to debug the solver, the ability for the imagery to precisely reflect the data is critical. In practice, the investigator interactively selects from a palette of visualization tools to construct a scene that can answer a query of the data. This is effective as long as the implicit contract of image quality between the individual and the visualization system is upheld. OpenGL rendering of scientific visualizations has worked remarkably well for exploratory visualization for most solver results. This is due to the consistency between the use of first-order representations in the simulation and the linear assumptions inherent in OpenGL (planar fragments and color-space interpolation). Unfortunately, the contract is broken when the solver discretization is of higher-order. There have been attempts to mitigate this through the use of spatial adaptation and/or texture mapping. These methods do a better job of approximating what the imagery should be but are not exact and tend to be view-dependent. This paper introduces new rendering mechanisms that specifically deal with the kinds of native data generated by high-order finite element solvers. The exploratory visualization tools are reassessed and cast in this system with the focus on image accuracy. This is accomplished in a GPU setting to ensure interactivity.
Flow Visualization with Quantified Spatial and Temporal Errors using Edge Maps|
H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci. In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 18, No. 9, IEEE Society, pp. 1383--1396. 2011.
Asymmetric Tensor Field Visualization for Surfaces|
G. Chen, D. Palke, Z. Lin, H. Yeh, P. Vincent, R.S. Laramee, E. Zhang. In IEEE Transactions on Visualization and Computer Graphics, Vol. 17, No. 12, IEEE, pp. 1979-1988. Dec, 2011.
Combinatorial Vector Field Topology in 3 Dimensions|
W. Reich, Dominic Schneider, Christian Heine, Alexander Wiebel, Guoning Chen, Gerik Scheuermann. In Mathematical Methods in Biomedical Image Analysis (MMBIA) Proceedings IEEE MMBIA 2012, pp. 47--59. November, 2011.
In this paper, we present two combinatorial methods to process 3-D steady vector fields, which both use graph algorithms to extract features from the underlying vector field. Combinatorial approaches are known to be less sensitive to noise than extracting individual trajectories. Both of the methods are a straightforward extension of an existing 2-D technique to 3-D fields. We observed that the first technique can generate overly coarse results and therefore we present a second method that works using the same concepts but produces more detailed results. We evaluate our method on a CFD-simulation of a gas furnace chamber. Finally, we discuss several possibilities for categorizing the invariant sets with respect to the flow.
Automatic Stream Surface Seeding|
M. Edmunds, T. McLoughlin, R.S. Laramee, G. Chen, E. Zhang, N. Max. In EUROGRAPHICS 2011 Short Papers, pp. 53--56. 2011.
|A wildland fire modeling and visualization environment,
J. Mandel, J.D. Beezley, A. Kochanski, V.Y. Kondratenko, L. Zhang, E. Anderson, J. Daniels II, C.T. Silva, C.R. Johnson. In Proceedings of the Ninth Symposium on Fire and Forest Meteorology, pp. (published online). 2011.
Simple and Efficient Mesh Layout with Space-Filling Curves|
H.T. Vo, C.T. Silva, L.F. Scheidegger, V. Pascucci. In Journal of Graphics, GPU, and Game Tools, pp. 25--39. 2011.
Feature-Based Statistical Analysis of Combustion Simulation Data|
J.C. Bennett, V. Krishnamoorthy, S. Liu, R.W. Grout, E.R. Hawkes, J.H. Chen, J. Shepherd, V. Pascucci, P.-T. Bremer. In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2011 IEEE Visualization Conference, Vol. 17, No. 12, pp. 1822--1831. 2011.
Branching and Circular Features in High Dimensional Data|
Bei Wang, B. Summa, V. Pascucci, M. Vejdemo-Johansson. In IEEE Transactions of Visualization and Computer Graphics (TVCG), Vol. 17, No. 12, pp. 1902--1911. 2011.
PubMed ID: 22034307
Large observations and simulations in scientific research give rise to high-dimensional data sets that present many challenges and opportunities in data analysis and visualization. Researchers in application domains such as engineering, computational biology, climate study, imaging and motion capture are faced with the problem of how to discover compact representations of high dimensional data while preserving their intrinsic structure. In many applications, the original data is projected onto low-dimensional space via dimensionality reduction techniques prior to modeling. One problem with this approach is that the projection step in the process can fail to preserve structure in the data that is only apparent in high dimensions. Conversely, such techniques may create structural illusions in the projection, implying structure not present in the original high-dimensional data. Our solution is to utilize topological techniques to recover important structures in high-dimensional data that contains non-trivial topology. Specifically, we are interested in high-dimensional branching structures. We construct local circle-valued coordinate functions to represent such features. Subsequently, we perform dimensionality reduction on the data while ensuring such structures are visually preserved. Additionally, we study the effects of global circular structures on visualizations. Our results reveal never-before-seen structures on real-world data sets from a variety of applications.
Keywords: Dimensionality reduction, circular coordinates, visualization, topological analysis
Combinatorial Laplacian Image Cloning|
A. Cuadros-Vargas, L.G. Nonato, V. Pascucci. In Proceedings of XXIV Sibgrapi – Conference on Graphics, Patterns and Images, pp. 236--241. 2011.
Seamless image cloning has become one of the most important editing operation for photomontage. Recent coordinate-based methods have lessened considerably the computational cost of image cloning, thus enabling interactive applications. However, those techniques still bear severe limitations as to concavities and dynamic shape deformation. In this paper we present novel methodology for image cloning that turns out to be highly efficient in terms of computational times while still being more flexible than existing techniques. Our approach builds on combinatorial Laplacian and fast Cholesky factorization to ensure interactive image manipulation, handling holes, concavities, and dynamic deformations during the cloning process. The provided experimental results show that the proposed technique outperforms existing methods in requisites such as accuracy and flexibility.
Experiences in Disseminating Educational Visualizations|
N. Andrysco, P. Rosen, V. Popescu, B. Benes, K.R. Gurney. In Lecture Notes in Computer Science (7th International Symposium on Visual Computing), Vol. 2, pp. 239--248. September, 2011.
Most visualizations produced in academia or industry have a specific niche audience that is well versed in either the often complicated visualization methods or the scientific domain of the data. Sometimes it is useful to produce visualizations that can communicate results to a broad audience that will not have the domain specific knowledge often needed to understand the results. In this work, we present our experiences in disseminating the results of two studies to national audience. The resulting visualizations and press releases allowed the studies’ researchers to educate a national, if not global, audience.
An Evaluation of 3-D Scene Exploration Using a Multiperspective Image Framework|
P. Rosen, V. Popescu. In The Visual Computer, Vol. 27, No. 6-8, Springer-Verlag New York, Inc., pp. 623--632. 2011.
PubMed ID: 22661796
PubMed Central ID: PMC3364594
Multiperspective images (MPIs) show more than what is visible from a single viewpoint and are a promising approach for alleviating the problem of occlusions. We present a comprehensive user study that investigates the effectiveness of MPIs for 3-D scene exploration. A total of 47 subjects performed searching, counting, and spatial orientation tasks using both conventional and multiperspective images. We use a flexible MPI framework that allows trading off disocclusion power for image simplicity. The framework also allows rendering MPI images at interactive rates, which enables investigating interactive navigation and dynamic 3-D scenes. The results of our experiments show that MPIs can greatly outperform conventional images. For searching, subjects performed on average 28% faster using an MPI. For counting, accuracy was on average 91% using MPIs as compared to 42% for conventional images.
Keywords: Interactive 3-D scene exploration, Navigation, Occlusions, User study, Visual interfaces
A User Study of Visualization Effectiveness Using EEG and Cognitive Load|
E.W. Anderson, K.C. Potter, L.E. Matzen, J.F. Shepherd, G.A. Preston, C.T. Silva. In Computer Graphics Forum, Vol. 30, No. 3, Note: Awarded 2nd Best Paper!, Edited by H. Hauser and H. Pfister and J.J. van Wijk, pp. 791--800. June, 2011.
Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.
Branching and Circular Features in High Dimensional Data|
SCI Technical Report, Bei Wang, B. Summa, V. Pascucci, M. Vejdemo-Johansson. No. UUSCI-2011-005, SCI Institute, University of Utah, 2011.
|Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications (Mathematics and Visualization),
Valerio Pascucci, Xavier Tricoche, Hans Hagen, Julien Tierny. Springer, 2011.
An End-to-End Framework for Evaluating Surface Reconstruction|
SCI Technical Report, M. Berger, J.A. Levine, L.G. Nonato, G. Taubin, C.T. Silva. No. UUSCI-2011-001, SCI Institute, University of Utah, 2011.
Non-Pinhole Approximations for Interactive Rendering|
P. Rosen, V. Popescu, K. Hayward, C. Wyman. In IEEE Computer Graphics and Applications, Vol. 99, 2011.
Edge Maps: Representing Flow with Bounded Error|
H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci. In Proceedings of IEEE Pacific Visualization Symposium 2011, Hong Kong, China, Note: Won Best Paper Award!, pp. 75--82. March, 2011.
Comparative Analysis of Multidimensional, Quantitative Data|
A. Lex, M. Streit, C. Partl, K. Kashofer, D. Schmalstieg. In IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 6, pp. 1027--1035. 2010.
When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a common task. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurations and use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methods and then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which show differences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is not desirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore, identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations, we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groups of dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in an arrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons between related records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depth comparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludes with two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second, biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informally showing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions.
Visual Links across Applications|
M. Waldner, W. Puff, A. Lex, M. Streit, D. Schmalstieg. In Proceedings of the Conference on Graphics Interface (GI '10), Canadian Human-Computer Communications Society, pp. 129--136. 2010.
The tasks carried out by modern information workers become increasingly complex and time-consuming. They often require to evaluate, interpret, and compare information from different sources presented in multiple application windows. With large, high resolution displays, multiple application windows can be arranged in a way so that a large amount of information is visible simultaneously. However, individual application windows' contents and visual representations are isolated and relations between information items contained in these windows are not explicit. Thus, relating and comparing information across applications has to be executed manually by the user, which is a tedious and error-prone task.